We developed a two-stage deep learning model, shown in Fig. REF , built upon the capsule network architecture [1]}, [2]}, which takes segmented lung regions as inputs. The first stage of the proposed model is a capsule network responsible of detecting slices with evidence of infection (caused by COVID-19 or CAP). Following a prior research [3]}, 10 most probable slices with infection, along with their infectious probability, are provided as input to the second stage, which analyzes all the candidates in parallel, making the decision by aggregating the outcome of each single candidate. The final output is the probability of the underlying case representing COVID-19, CAP, or normal classes. While the first stage model is trained on slice-level labels provided by an experienced radiologist, the second stage is trained on patient-level labels. Expanding the proposed deep learning model, we incorporated clinical data, i.e., the output of the deep learning model is merged with clinical data and fed to a multi-layer perceptron (MLP), shown in Fig. REF , to make the final diagnosis.
<FIGURE><FIGURE><TABLE>